Traffic Signal Cycle Control with Centralized Critic and Decentralized Actors under Varying Intervention Frequencies
Not on the map. Why?
Abstract (arXiv)
Traffic congestion in urban areas is a significant problem, leading to prolonged travel times, reduced efficiency, and increased environmental concerns. Effective traffic signal control (TSC) is a key strategy for reducing congestion. Unlike most TSC systems that rely on high-frequency control, this study introduces an innovative joint phase traffic signal cycle control method that operates effectively with varying control intervals. Our method features an adjust all phases action design, enabling simultaneous phase changes within the signal cycle, which fosters both immediate stability and sustained TSC effectiveness, especially at lower frequencies. The approach also integrates decentralized actors to handle the complexity of the action space, with a centralized critic to ensure coordinated phase adjusting. Extensive testing on both synthetic and real-world data across different intersection types and signal setups shows that our method significantly outperforms other popular techniques, particularly at high control intervals. Case studies of policies derived from traffic data further illustrate the robustness and reliability of our proposed method.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Jun 2024.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2024: 1; 2025: 2; 2026: 3.
6 papers.
- 2024iLLM-TSC: Integration reinforcement learning and large language model for traffic signal control policy improvementcited by 23
- 2025VLMLight: Safety-Critical Traffic Signal Control via Vision-Language Meta-Control and Dual-Branch Reasoning Architecturecited by 3
- 2025Unicorn: A Universal and Collaborative Reinforcement Learning Approach Towards Generalizable Network-Wide Traffic Signal Controlcited by 2
- 2026LATS: Large Language Model Assisted Teacher-Student Framework for Multi-Agent Reinforcement Learning in Traffic Signal Controlcited by 1
- 2026A Robust and Efficient Multi-Agent Reinforcement Learning Framework for Traffic Signal Controlcited by 0
- 2026ReasonLight: A Multimodal Foundation Model-Enhanced Reinforcement Learning Framework for Zero-Shot Traffic Signal Controlcited by 0
- 2026ReasonLight: A Multimodal Foundation Model-Enhanced Reinforcement Learning Framework for Zero-Shot Traffic Signal Control28 May 2026
- 2026LATS: Large Language Model Assisted Teacher-Student Framework for Multi-Agent Reinforcement Learning in Traffic Signal Control25 Mar 2026
- 2026A Robust and Efficient Multi-Agent Reinforcement Learning Framework for Traffic Signal Control12 Mar 2026
- 2025VLMLight: Safety-Critical Traffic Signal Control via Vision-Language Meta-Control and Dual-Branch Reasoning Architecture26 May 2025
- 2025Unicorn: A Universal and Collaborative Reinforcement Learning Approach Towards Generalizable Network-Wide Traffic Signal Control14 Mar 2025
- 2024iLLM-TSC: Integration reinforcement learning and large language model for traffic signal control policy improvement8 Jul 2024